Hey! Exam season is coming up. Here’s some of the best resources to help you reach that first class degree!

PS: There’s still no adverts on this.

I’m still filling out and doing some of the stuff, so bare with me (like Quizlet not being done / only completing 2 of Pauls tutorials)

Here’s an exam time table courtsey by one of your other course reps, Huba

Algorithms

Here is every single class test, every single question Pruduence has asked us + general knowledge and flashcards you need to know:

https://quizlet.com/_4qnu7e

To test yourself, click “test” to generate a practice test. Quizlet requires an account but if you want flashcards tailored for this course then it’s good. The flashcards can be done backwards too, so you get the answer and you have to figure out what the question is referencing.

Big O notation is a big part of this module. Every lecture she’s presented us with a program and asked us what the psuedocode is. So you can guess that this will come up in the exam. For that reason, I’ve written a seperate blog post here:

Of course I have these qustions in my quizlet for this module.

Here’s every single thing we’ve done in this module:

https://brandonskerritt.github.io/university/algorithms/

Here’s a blog post on Linked Lists (featuring blockchain)

https://hackernoon.com/you-dont-understand-blockchain-unless-you-understand-this-simple-data-structure-fb1df7982cc5

Insertion sort and selection sort are the same

Paul - comp116 - Mathematics

So Paul’s module is… interesting. His tutorials aren’t so useful for exams (as they don’t exactly match the exam content) so I’m going to list a lot of resources here.

Paul’s module is broken down into

• Vectors & Matrices
• Calculus
• Complex Numbers
• Statistics
• Returns back to matrices
• Information Theory (which I am not sure whether he will cover, so best to see what he says about this one)

You can therefore group these into 4 main topics: Vectors & Matrices, Calculus, Complex Numbers and Statistics. Matrices and Vectors is called Linear Algebra.

Let’s go through and provide resources for every single one:

Numbers

Although this is relatively easy, here’s a blog post on it:

https://brandonskerritt.github.io/n9opumbers/

Polynomials

https://brandonskerritt.github.io/maths/polynomials/

Linear Algebra

3blue1brown’s videos are really good for getting an intution at how linear algebra actually works:

Vectors, a short guide

https://brandonskerritt.github.io/university/vectors-short/

Linear Algebra, a blog post by me

https://brandonskerritt.github.io/university/Linear-Algebra/

Calculus:

https://brandonskerritt.github.io/university/Calculus/

Here’s a good 3blue1brown series on it

Complex Numbers

https://brandonskerritt.github.io/university/complex-numbers/

I quite like this video series on complex numbers too:

Statistics

https://brandonskerritt.github.io/university/probability-statistics/

Let’s actually go super in depth here:

# numbers and polynomials

what was covered:

• different types of numbers ((natural, whole, rational) DONE
• polynomials and their form and properties (degree, coefficients, roots) DONE

# vectors

the notion of n-vectors as na ordered collection of numbers; basic vector operations (addition, so called scalar multiplcation), size.

• scalar vectors DONE
• size of a vector (pythag therom) DONE
• collections of vectors (vector spaces)
• low level matrix operations
• 2x2 matrices
• 3x3 matrices
• matrix vector products
• linear transformations

# calculus

• functions
• properties of linear functions
• first derivative of f(x) https://www.youtube.com/watch?v=rAof9Ld5sOg
• limits
• the idea of a turning point
• local minimal and maximal behaviors
• extension to the concept of optimisation problem
• integral calculus
• anti-derivative
• area under a curve

# Complex numbers

• different representations of complex numbers (basic form, co-ordinate (Argand), polar form, Euler (or Exponential) form)
• operations on complex numbers
• multiplcation
• division
• complex conjugate and modulus (size) of a complex number

# Statistics

• Difference between probability and statistics
• ideas of expected outcome
• _Distribution_
• confidence intervals
• methods for distinguishing “significane” of experimental outcomes

# linear algebra and matrix theory

• n x n matrices
• matrix operation transpose
• product of an n x m matrix A with an m x k matrix B
• properties of matrix product
• notion of identity matrix
• notion of inverse matrix
• singular matrices
• determinant of a matrix
• how to calculate determinant and inverse
• intro to spectral analysis
• eigenvalues and eigen vectors

Paul’s tutorials

Here is the repo I use for his tutorials:

https://github.com/brandonskerritt/Computer-Science/tree/master/Year-1-Semester-2/comp116/Tutorials

Quizlet

https://quizlet.com/286679202/analytic-techniques-flash-cards

David Jackson - Computer Systems

Dave’s module seems to be less about memorising things and more about knowing facts and how things work and answering edge case questions based on these facts. So memorisation plays a key part here but so does knowing how things work.

This is a quizlet featuring some questions he’s asked in lectures + other things

https://quizlet.com/286653457/computer-systems-first-year-flash-cards/

Here are my notes on this module:

Basics of computer systems https://brandonskerritt.github.io/university/computer-systems-1/

The stack (features on both computer systems and algorithms) https://brandonskerritt.github.io/university/stack/

Operating systems https://brandonskerritt.github.io/university/operating-systems/

Concurrent programming https://brandonskerritt.github.io/university/concurrent-programming/

Memory https://brandonskerritt.github.io/university/computer_systems/

Files https://brandonskerritt.github.io/university/files/

Devices https://brandonskerritt.github.io/university/devices/

Compilers https://brandonskerritt.github.io/university/compiler/

Java - Russel

No exam for Russel :)

Extras:

How do you study?